Reinforced Extractive Summarization with Question-Focused Rewards

TL;DR

This paper introduces a reinforcement learning approach for extractive summarization that uses question-focused rewards derived from Cloze questions to improve summary quality, surpassing existing methods.

Contribution

The paper proposes a novel training paradigm converting human abstracts into Cloze questions and employing question-focused rewards for better extractive summaries.

Findings

Outperforms state-of-the-art summarization systems

Effective in producing concise and informative summaries

Utilizes reinforcement learning with question-based rewards

Abstract

We investigate a new training paradigm for extractive summarization. Traditionally, human abstracts are used to derive goldstandard labels for extraction units. However, the labels are often inaccurate, because human abstracts and source documents cannot be easily aligned at the word level. In this paper we convert human abstracts to a set of Cloze-style comprehension questions. System summaries are encouraged to preserve salient source content useful for answering questions and share common words with the abstracts. We use reinforcement learning to explore the space of possible extractive summaries and introduce a question-focused reward function to promote concise, fluent, and informative summaries. Our experiments show that the proposed method is effective. It surpasses state-of-the-art systems on the standard summarization dataset.